Predicting Helpful Posts
Original paper: https://www.aclweb.org/anthology/N19-1318
Here is a quick summary:
The research purpose is to identify helpful posts from discussion threads in forums, especially long-running discussions.
The approach is to model the relevance of each post concerning the original post and the novelty (not presented in the earlier posts of the discussion thread)of a post based on a windowed context.
To model, the 'relevance' the original post and the target post are encoded using an RNN (GRU). The encoded sequences are then element-wise multiplied.
As for modeling of the 'novelty,' the target post and the past K posts (where K is the number of past posts taken into context. A 'K' between 11 to 7 worked best for the Reddit dataset used in the experiment - performance stops improving after a certain number of posts taken into context) are also encoded using the same RNN text encoder. Once the 'K' posts are text encoded it is then fed thru another RNN encoder to encode the sequence of the posts before combining it with the target post encoding thru element-wise multiplication.
The output of both the 'relevance' and 'novelty' are then concatenated then fed thru a fully connected layer. (Please see network diagram)
This approach determines if a new post to a discussion is helpful because of the two factors considered: 1. relevance of the new post to the discussion 2. the novelty of the information introduced to the discussion.
The approach outperforms current methods of sequence classification because of this approach.
Here is a quick summary:
The research purpose is to identify helpful posts from discussion threads in forums, especially long-running discussions.
The approach is to model the relevance of each post concerning the original post and the novelty (not presented in the earlier posts of the discussion thread)of a post based on a windowed context.
To model, the 'relevance' the original post and the target post are encoded using an RNN (GRU). The encoded sequences are then element-wise multiplied.
As for modeling of the 'novelty,' the target post and the past K posts (where K is the number of past posts taken into context. A 'K' between 11 to 7 worked best for the Reddit dataset used in the experiment - performance stops improving after a certain number of posts taken into context) are also encoded using the same RNN text encoder. Once the 'K' posts are text encoded it is then fed thru another RNN encoder to encode the sequence of the posts before combining it with the target post encoding thru element-wise multiplication.
The output of both the 'relevance' and 'novelty' are then concatenated then fed thru a fully connected layer. (Please see network diagram)
This approach determines if a new post to a discussion is helpful because of the two factors considered: 1. relevance of the new post to the discussion 2. the novelty of the information introduced to the discussion.
The approach outperforms current methods of sequence classification because of this approach.
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